{"type":"ScholarlyArticle","title":"Mixed Causal Structure Discovery with Application to Prescriptive Pricing","authors":[{"name":"Wei Wenjuan","slug":"wei-wenjuan","given":"Wei","family":"Wenjuan","affiliation":"NEC Labs China"},{"name":"Feng Lu","slug":"feng-lu","given":"Feng","family":"Lu","affiliation":"NEC Labs China"},{"name":"Liu Chunchen","slug":"liu-chunchen","given":"Liu","family":"Chunchen","affiliation":"NEC Labs China"}],"abstract":"Prescriptive pricing is one of the most advanced pricing techniques, which derives the optimal price strategy to maximize the future profit/revenue by carrying out a two-stage process, demand modeling and price optimization.Demand modeling tries to reveal price-demand laws by discovering causal relationships among demands, prices, and objective factors, which is the foundation of price optimization.Existing methods either use regression or causal learning for uncovering the price-demand relations, but suffer from pain points in either accuracy/efficiency or mixed data type processing, while all of these are actual requirements in practical pricing scenarios.This paper proposes a novel demand modeling technique for practical usage.Speaking concretely, we propose a new locally consistent information criterion named MIC,and derive MIC-based inference algorithms for an accurate recovery of causal structure on mixed factor space.Experiments on simulate/real datasets show the superiority of our new approach in both price-demand law recovery and demand forecasting, as well as show promising performance in supporting optimal pricing.","doi":"10.24963/ijcai.2018/711","url":"https://ijcai.org/proceedings/2018/711","pdf":"https://ijcai.org/proceedings/2018/0711.pdf","markdown":"https://ijcai.org/proceedings/2018/711.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/711","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Uncertainty in AI","track":"Main track","number":711,"pages":{"first":5126,"last":5134},"keywords":["Machine Learning Applications: Other Applications","Uncertainty in AI: Relational Inference","Machine Learning: Interpretability"],"videos":[],"references":[{"position":1,"raw":"[Bühlmann et al., 2014] Peter Bühlmann, Jonas Peters, and Jan Ernest. 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